Upload README.md

README.md

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+---
+license: apache-2.0
+tags:
+  - object-detection
+  - oriented-object-detection
+  - jittor
+  - remote-sensing
+  - dota
+---
+
+# Point2RBox-v3 (Jittor reproduction)
+
+This is a **Jittor** reproduction of
+[Point2RBox-v3](https://github.com/VisionXLab/Point2RBox-v3)
+(point-supervised oriented object detection with SAM-guided pseudo-label
+refinement, [arXiv:2509.26281](https://arxiv.org/pdf/2509.26281)), built on
+JDet. Code: <https://github.com/mingqian-233/Point2RBox-v3-jittor>.
+
+## Results (DOTA-v1.0, official test server, mAP50)
+
+| Model | Paper (PyTorch) | This repo (Jittor) |
+|---|---|---|
+| Point2RBox-v3 end-to-end (12 ep) | 61.38 | **59.52** |
+| Point2RBox-v3 two-stage (rotated-FCOS) | 67.24 | **TBD（Task1 包已生成，待服务器）** |
+
+The end-to-end number is the official DOTA-v1.0 Task1 test-server mAP50
+(`0.5952330691632053`) and passes the project's paper−2.0 acceptance threshold
+(59.38). Per-class AP table: TBD.
+
+Local diagnostic only: the norm-eval-correct end-to-end checkpoint scores
+**66.6056 mAP50** on the unmerged 1024×1024 trainval-patch protocol. The
+same-machine full upstream PyTorch run scores **65.60**, while the published
+upstream log reports **66.70**. This is not the official DOTA test-server
+protocol; the merged test submission has been generated and awaits server
+evaluation.
+
+The completed two-stage checkpoint scores **75.7029 mAP50** on the same local
+trainval-patch diagnostic. Its canonical Task1 submission has been generated;
+the official DOTA test-server result is pending.
+
+The generated stage-2 pseudo boxes were also matched directly against all
+245,953 trainval GT boxes. They achieve mean rotated IoU **0.7330**,
+recall@0.5 **91.03%**, and recall@0.75 **56.58%**. The corresponding
+official-code PyTorch v2 baseline is 0.7295 / 91.25% / 55.39%.
+
+## Files
+
+- `checkpoints/point2rbox_v3_1x_dota_ckpt_12.pkl` — end-to-end model
+- `checkpoints/rotated_fcos_1x_dota_using_pseudo_ckpt_12.pkl` — stage-2 model
+- `weights/mobile_sam.pkl` — MobileSAM converted weights (439 tensors, numpy
+  pickle; native Jittor port, mask IoU 1.0000 vs PyTorch on CPU fp32)
+- `weights/ted.pkl` — TED edge detector converted weights (parity 5.7e-6)
+- `logs/` — training logs
+
+## Training environment
+
+- Jittor 1.3.8.5, numpy 1.26.4 (pinned — numpy≥2 silently corrupts jt.array
+  inputs), CUDA 11.2 toolchain (g++-10), single A100 80GB, official
+  hyper-parameters verbatim (end-to-end batch=2; stage-2 batch=4; AdamW
+  5e-5, grad-clip 35, 12 epochs, LinearLR warmup 500 iters + MultiStepLR
+  [8,11]).
+- Config parity is locked by a zero-tolerance L0 test suite (17 tests,
+  including the 74-line per-class SAM rule table byte-copied from upstream).
+
+## Known differences from the upstream paper run
+
+1. **SAM attention bias**: the upstream `mobile_sam` package calls `eval()`
+   before `load_state_dict`, leaving TinyViT's non-persistent attention-bias
+   cache at its random initialisation — i.e. the upstream training used an
+   unseeded random attention bias (unreproducible across runs). This repo
+   loads the weights correctly. Direction of the effect on mAP is unknown
+   (expected neutral-to-positive). See `docs/config_parity.md`.
+2. Upstream's copy-paste stage-2 branch is dead code (condition can never be
+   true); reproduced verbatim.
+3. `num_workers=0` instead of 2 (Jittor multi-process dataloader deadlock);
+   pure infra, no effect on training math.
+4. GPU-mode numeric noise vs PyTorch golden is at the cuDNN-TF32 level
+   (~1e-3); CPU-mode strict-fp32 parity is 1e-5 to bit-exact per component.
+
+## Citation
+
+Please cite the original papers:
+
+```bibtex
+@article{point2rboxv3,
+  title={Point2RBox-v3: Self-Bootstrapping from Point Annotations via
+         Integrated Pseudo-Label Refinement and Utilization},
+  journal={arXiv preprint arXiv:2509.26281}, year={2025}
+}
+@article{whollywood,
+  title={Wholly-WOOD: Wholly Leveraging Diversified-quality Labels for
+         Weakly-supervised Oriented Object Detection},
+  year={2024}
+}
+```